Prediction of S&P 500 and DJIA stock indices using Particle Swarm Optimization technique

The present paper introduces the particle swarm optimization (PSO) technique to develop an efficient forecasting model for prediction of various stock indices. The connecting weights of the adaptive linear combiner based model are optimized by the PSO so that its mean square error(MSE) is minimized. The short and long term prediction performance of the model is evaluated with test data and the results obtained are compared with those obtained from the multilayer perceptron (MLP) based model. It is in general observed that the proposed model is computationally more efficient, prediction wise more accurate and takes less training time compared to the standard MLP based model.

[1]  Robert E. Dorsey,et al.  Genetic algorithms for estimation problems with multiple optima , 1995 .

[2]  K. Warwick,et al.  Dynamic recurrent neural network for system identification and control , 1995 .

[3]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[4]  Christopher J. Neely,et al.  Is Technical Analysis in the Foreign Exchange Market Profitable? A Genetic Programming Approach , 1996, Journal of Financial and Quantitative Analysis.

[5]  Y. Hiemstra Applying Neural Networks and Genetic Algorithms to Tactical Asset Allocation , 1996 .

[6]  M. Lettau Explaining the facts with adaptive agents: The case of mutual fund flows , 1997 .

[7]  Hitoshi Iba,et al.  Using genetic programming to predict financial data , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[8]  Rui Jiang,et al.  Discovering investment strategies in portfolio management: a genetic algorithm approach , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

[9]  Baikunth Nath,et al.  A fusion model of HMM, ANN and GA for stock market forecasting , 2007, Expert Syst. Appl..